Experiments On Adaptation-Guided Retrieval
In Case-Based Design

Barry Smyth1 and Mark T. Keane2

1 Dublin Laboratory, Trinity College, Dublin 2, IRELAND
(E-mail: barry.smyth@hdl.ie)
2 Department of Computer Science, Trinity College, Dublin 2, IRELAND

Abstract. Case-based reasoning (CBR) has been applied with some success to
complex planning and design tasks. In such systems, the best case is retrieved and
adapted to solve a particular target problem. Often, the best case is that which can
be most easily adapted to the target problem (as the overhead in adaptation is
generally very high). Standard CBR systems use semantic-similarity to retrieve
cases, on the assumption that the most similar case is the easiest case to adapt.
However, this assumption can be shown to be flawed. In this paper, we report a
novel retrieval method, called adaptation-guided retrieval, that is sensitive to the
ease-of-adaptation of cases. In the context of a CBR system for software-design,
called Dj Vu, we show through a series of experiments that adaptation-guided
retrieval is more accurate than standard retrieval techniques, that it scales well to
large case-bases and that it results in more efficient overall problem-solving
performance. The implications of this method and these results are discussed.


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